



from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
	import PIL

if is_torch_available():
	import torch


a      :  Any			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = True,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     size if size is not None else {"""shortest_edge""": 224}
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		int							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		Tuple							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		int							      =     resample
						UpperCAmelCase_:		Dict							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Optional[int]							      =     do_center_crop
						UpperCAmelCase_:		List[str]							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_flip_channel_order

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BILINEAR,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		List[str]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_					)
						if "shortest_edge" not in size:
									raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'					)
						UpperCAmelCase_:		int							      =     get_resize_output_image_size(SCREAMING_SNAKE_CASE_,   size=size["""shortest_edge"""],   default_to_square=SCREAMING_SNAKE_CASE_					)
						return resize(SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		str							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   Optional[Any]:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None					)       ->   np.ndarray:
						return flip_channel_order(SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		List[Any]							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		List[Any]							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		str							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		Union[str, Any]							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		Optional[Any]							      =     (
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

						UpperCAmelCase_:		Optional[int]							      =     size if size is not None else self.size
						UpperCAmelCase_:		List[str]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		str							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		Optional[int]							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None:
									raise ValueError("""Size must be specified if do_resize is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		str							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		List[Any]							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Any							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		int							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						# the pretrained checkpoints assume images are BGR, not RGB
						if do_flip_channel_order:
									UpperCAmelCase_:		Optional[int]							      =     [self.flip_channel_order(image=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		str							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[int]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Dict							      =     outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(SCREAMING_SNAKE_CASE_					) != len(SCREAMING_SNAKE_CASE_					):
												raise ValueError(
												    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""					)

									if is_torch_tensor(SCREAMING_SNAKE_CASE_					):
												UpperCAmelCase_:		Any							      =     target_sizes.numpy()

									UpperCAmelCase_:		str							      =     []

									for idx in range(len(SCREAMING_SNAKE_CASE_					)					):
												UpperCAmelCase_:		str							      =     torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0					),   size=target_sizes[idx],   mode="""bilinear""",   align_corners=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		int							      =     resized_logits[0].argmax(dim=0					)
												semantic_segmentation.append(SCREAMING_SNAKE_CASE_					)
						else:
									UpperCAmelCase_:		List[Any]							      =     logits.argmax(dim=1					)
									UpperCAmelCase_:		Any							      =     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

						return semantic_segmentation











import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a      :  Optional[Any]			     =			logging.getLogger(__name__)







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None
			A      		=	None


if is_torch_available():
	import torch
	from torch.utils.data import Dataset







	class 				_a				(   _lowerCAmelCase					):
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Union[str, Any]:
							UpperCAmelCase_:		Optional[Any]							      =     hans_processors[task]()

							UpperCAmelCase_:		Dict							      =     os.path.join(
							    SCREAMING_SNAKE_CASE_,   """cached_{}_{}_{}_{}""".format(
							        """dev""" if evaluate else """train""",   tokenizer.__class__.__name__,   str(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_,   ),   )
							UpperCAmelCase_:		Dict							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		str							      =     label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							UpperCAmelCase_:		int							      =     cached_features_file + """.lock"""
							with FileLock(SCREAMING_SNAKE_CASE_					):
										if os.path.exists(SCREAMING_SNAKE_CASE_					) and not overwrite_cache:
													logger.info(f'Loading features from cached file {cached_features_file}'					)
													UpperCAmelCase_:		Dict							      =     torch.load(SCREAMING_SNAKE_CASE_					)
										else:
													logger.info(f'Creating features from dataset file at {data_dir}'					)

													UpperCAmelCase_:		Union[str, Any]							      =     (
													    processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
													)

													logger.info("""Training examples: %s""",   len(SCREAMING_SNAKE_CASE_					)					)
													UpperCAmelCase_:		List[Any]							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
													logger.info("""Saving features into cached file %s""",   SCREAMING_SNAKE_CASE_					)
													torch.save(self.features,   SCREAMING_SNAKE_CASE_					)

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   List[str]:
							return self.label_list


if is_tf_available():
	import tensorflow as tf







	class 				_a				:
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = 128,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Optional[int]:
							UpperCAmelCase_:		Union[str, Any]							      =     hans_processors[task]()
							UpperCAmelCase_:		List[Any]							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		Dict							      =     label_list

							UpperCAmelCase_:		List[str]							      =     processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
							UpperCAmelCase_:		Any							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

							def gen():
										for ex_index, ex in tqdm.tqdm(enumerate(self.features					),   desc="""convert examples to features"""					):
													if ex_index % 10000 == 0:
																logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_					))					)

													yield (
													    {
													        "example_id": 0,
													        "input_ids": ex.input_ids,
													        "attention_mask": ex.attention_mask,
													        "token_type_ids": ex.token_type_ids,
													    },
													    ex.label,
													)

							UpperCAmelCase_:		List[str]							      =     tf.data.Dataset.from_generator(
							    SCREAMING_SNAKE_CASE_,   (
							        {
							            """example_id""": tf.intaa,
							            """input_ids""": tf.intaa,
							            """attention_mask""": tf.intaa,
							            """token_type_ids""": tf.intaa,
							        },
							        tf.intaa,
							    ),   (
							        {
							            """example_id""": tf.TensorShape([]					),
							            """input_ids""": tf.TensorShape([None, None]					),
							            """attention_mask""": tf.TensorShape([None, None]					),
							            """token_type_ids""": tf.TensorShape([None, None]					),
							        },
							        tf.TensorShape([]					),
							    ),   )

				def      __snake_case       (self					)       ->   Optional[Any]:
							return self.dataset

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   Union[str, Any]:
							return self.label_list







class 				_a				(   _lowerCAmelCase					):

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_train_set.txt"""					)					),   """train"""					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_evaluation_set.txt"""					)					),   """dev"""					)

			def      __snake_case       (self					)       ->   Optional[int]:
						return ["contradiction", "entailment", "neutral"]

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     []
						for i, line in enumerate(SCREAMING_SNAKE_CASE_					):
									if i == 0:
												continue
									UpperCAmelCase_:		Tuple							      =     """%s-%s""" % (set_type, line[0])
									UpperCAmelCase_:		int							      =     line[5]
									UpperCAmelCase_:		int							      =     line[6]
									UpperCAmelCase_:		List[str]							      =     line[7][2:] if line[7].startswith("""ex"""					) else line[7]
									UpperCAmelCase_:		Any							      =     line[0]
									examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_,   text_a=SCREAMING_SNAKE_CASE_,   text_b=SCREAMING_SNAKE_CASE_,   label=SCREAMING_SNAKE_CASE_,   pairID=SCREAMING_SNAKE_CASE_					)					)
						return examples







def  lowerCAmelCase_	(lowerCAmelCase__:		List[InputExample]		,						lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		PreTrainedTokenizer		,						):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     {label: i for i, label in enumerate(lowerCAmelCase__  )}

			UpperCAmelCase_:		Optional[int]							      =     []
			for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__  )		,						desc="""convert examples to features"""  ):
						if ex_index % 1_0_0_0_0 == 0:
									logger.info("""Writing example %d""" % (ex_index)  )

						UpperCAmelCase_:		Tuple							      =     tokenizer(
						    example.text_a		,						example.text_b		,						add_special_tokens=lowerCAmelCase__		,						max_length=lowerCAmelCase__		,						padding="""max_length"""		,						truncation=lowerCAmelCase__		,						return_overflowing_tokens=lowerCAmelCase__		,						)

						UpperCAmelCase_:		Optional[Any]							      =     label_map[example.label] if example.label in label_map else 0

						UpperCAmelCase_:		Tuple							      =     int(example.pairID  )

						features.append(InputFeatures(**lowerCAmelCase__		,						label=lowerCAmelCase__		,						pairID=lowerCAmelCase__  )  )

			for i, example in enumerate(examples[:5]  ):
						logger.info("""*** Example ***"""  )
						logger.info(F'guid: {example}'  )
						logger.info(F'features: {features[i]}'  )

			return features


a      :  Optional[int]			     =			{
    'hans': 3,
}

a      :  Dict			     =			{
    'hans': HansProcessor,
}






